Mapping the plastic legacy with NIXVEGS: Machine-learning enabled microplastics prediction in sediments

Aquatic sediments act as major sinks for microplastic (MP) pollution, yet accurately quantifying their MP distributions and inventories on a regional scale remains challenging, especially in hydrodynamically complex estuarine environments. Existing distribution models focus on ocean basins or simple hydrodynamic systems but lack sufficient adaptability to the spatial heterogeneity typical of estuaries. To address this limitation, we developed NIXVEGS, an open-source empirical spatial prediction pipeline adapting machine learning components like nested cross-validation and ensemble modeling specifically for the sparse data characteristic of environmental MP studies. It integrates granulometric proxies and spatio-temporal connectivity to predict MP distributions and provides data-driven decisions in model selection combined with performance validation on unseen data under the best possible utilization of the available observations. The model predictions increased spatial data coverage 7.6-fold from an original set of 26 analyzed MP sediment concentration in the Schlei estuary (Baltic Sea coast, Northern Germany). We demonstrate the application of NIXVEGS and use its predictions to estimate the region’s sedimentary MP inventory at ~ 20 trillion particles (50-5000 µm) or ~ 14.3 tons. Estuarine sediments are key environments for monitoring MP pollution evolution, where NIXVEGS can assist regional plastic management and contribute to the understanding of a global MP budget.